A Graph-Based Technique for the Automated Control-Oriented Modeling of District Heating Networks

TL;DR

The work addresses the need for scalable, control-oriented models for district heating networks with building-level control by introducing a graph-based framework that assembles a state-space model from component blocks and topology, supported by an algebraic calculation of flows. It validates the approach on a lab-scale network, achieving accurate temperature and mass-flow representations, and demonstrates rapid model generation across configurations for topology optimization. The paper formulates and solves an energy-minimizing design problem via branch-and-bound optimum branching, showing a 15% reduction in enthalpy losses compared to a conventional length-minimized layout, underscoring practical energy-efficiency benefits. The proposed framework enables fast reconfiguration for distributed control and paves the way for MPC applications and integration of renewables in large-scale DHNs.

Abstract

Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an average normalized root mean square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology.

Figures (12)

• Figure 1: Sample six user DHN.
• Figure 2: Components of a single user loop with and without bypass segment.
• Figure 3: Layout of a two-user DHN with two parallel branches.
• Figure 4: Configurations of the two types of branches.
• Figure 5: Diagram of the two-user lab-scale DHN with sensor locations labeled.